International Journal of
Environmental Research
and Public Health
Article
Factors Affecting the Cases and Deaths of COVID-19 Victims
Jerald M. Velasco 1, *, Wei-Chun Tseng 1 and Chia-Lin Chang 1,2
1
2
*
Department of Applied Economics, National Chung Hsing University, Taichung 402, Taiwan;
tweichun@nchu.edu.tw (W.-C.T.); changchialin@email.nchu.edu.tw (C.-L.C.)
Department of Finance, National Chung Hsing University, Taichung 402, Taiwan
Correspondence: jerald.m.velasco@isu.edu.ph; Tel.: +886-978-453-826
Abstract: This paper attempts to find the factors that affect the number of cases and deaths of
coronavirus disease 2019 (COVID-19) patients a year after the first outbreak in Wuhan, China. There
were 141 countries affected with COVID-19 involved in the study. Countries were grouped based
on population. Using ordinary least squares regression, it was found that the total number of cases
and deaths were significantly related with the levels of population of the different countries. On
the overall, median age of the country, and average temperature are positively related with the
number of deaths from the virus. On the other hand, population density is positively related with
the deaths due to COVID for low populated countries. The result of this preliminary study can be
used as a benchmark for authorities in the formulation of policies with regards to treating COVID-19
related issues.
Keywords: COVID-19; populations; tests; temperature; cases; deaths
1. Introduction
Citation: Velasco, J.M.; Tseng, W.-C.;
Chang, C.-L. Factors Affecting the
Cases and Deaths of COVID-19
Victims. Int. J. Environ. Res. Public
Health 2021, 18, 674. https://
doi.org/10.3390/ijerph18020674
Received: 24 December 2020
Accepted: 6 January 2021
Published: 14 January 2021
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
In the midst of pandemic caused by severe acute respiratory syndrome–coronavirus 2
(SARS-CoV-2), now popularly referred to as coronavirus disease 2019 (COVID-19), many
strategies have been adopted by governments as to how to fight this illness that has
affected many lives. Non-pharmaceutical interventions (NPIs) are being implemented to
eradicate the disease but these have just delayed and moderated the spread of the virus
since vaccines and antiviral drugs have yet to be discovered [1]. A variety of regulations
has been designed, among which include restriction of non-essential travel, imposition of
social distancing, declaration of lockdowns on big cities and banning mass gatherings, all
supposedly to prevent the spread of the novel corona virus [2].
The COVID-19 disease was first identified in Wuhan, China, in early December 2019
with the early cases reported in the city resulting in its spread worldwide that brought
negatively significant changes to the normal lives of people in different countries of the
world. Different laboratories and research institutions correspondingly responded and are
in a race to discover solutions or a vaccine for the cure of this virus. As a result, there are
more than 50 COVID-19 vaccine candidates under clinical trials [3].
As of 12 December 2020, there were already 71,437,016 cases recorded worldwide
that resulted to 1,601,163 casualties [4]. There were 49,636,518 people who had already
recovered from the disease not because of vaccines developed but only with the use of
treatment for the symptoms of the patients. At this time of crisis, where vaccines were
limited and most of them are in clinical trials, early prevention for such diseases is better
than cure. Many states already conducted mass testing to determine the presence of viruses
in different areas. But there are some arguments that mass testing is not necessary for their
country. In South Korea, exploratory data analysis was used in assessing how effective
mass testing is. According to the result, they noticed that 33 days after the first infection in
the country, a significant increase in the number of tests resulted from a significant increase
in the identified number of infected cases. This result allows the government to take early
Int. J. Environ. Res. Public Health 2021, 18, 674. https://doi.org/10.3390/ijerph18020674
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2021, 18, 674
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actions to focus more efforts in containing the breakout that will eventually happen [5].
According to Facundo, P. and Shi L., testing is a very close substitute for quarantine and
can substantially reduce the need for indiscriminate quarantines [6]. With this, some
rich countries have already administered mass testing like Germany, which carried out
at least 500,000 tests a week as soon as the virus entered its territory [7]. However, some
policy makers also says that infection rate does not justify mass testing there is no need
for mass testing for COVID-19 in Taiwan as the rate of infection so far has been quite
low [8]. Though in different countries have different scenarios, where number of cases and
death rates varies, it is important to know what the different factors which may affect these
numbers are.
Many researches have been published regarding the behavior, effects on humans,
development of vaccines and etc., but not much research in assessing the number of deaths
and cases of the said virus. This paper aims to provide an answer to the question: should a
country provide mass testing? Is it necessary in our fight against COVID-19? Basically, this
study investigates the factors affecting the number of deaths and cases of COVID-19 a year
since the 1st outbreak occurred in Wuhan, the capital of the province of Hubei, China.
Literature Review
A pandemic includes diseases of very different etiologies that exhibit a variety of
epidemiologic features as applied to important global events spanning many centuries [9].
In the past few years, many diseases have appeared and threatened all the people worldwide. Before the appearance of this severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), some countries had already suffered from severe acute respiratory syndrome coronavirus (SARS-CoV) which is an infectious disease that caught global attention
in the 21st century [10] and Middle East respiratory syndrome coronavirus (MERS-CoV).
Asia and other parts of the world were affected by SARS-CoV in 2003 while MERS-CoV
had a first outbreak in Saudi Arabia (2012 and 2018) and larger outbreaks occurred in South
Korea in 2015 [11].
To avoid the wide spread of those diseases, equipment and new tools were developed
to detect people who are showing symptoms either SARS or MERS. In 2003, there was no
vaccine developed yet for SARS, but it was recommended by the World Health Organization (WHO) [12] that containment measures are based on: surveillance network including
early warning system; isolation of suspected or probable cases, voluntary or mandatory
quarantine of suspected contacts for 10 days and exit screening for outgoing passengers
from areas with recent local transmission by asking questions and temperature measurement. Infrared (IR) cameras were also used to detect subjects with fever, the basic symptom
of SARS and avian influenza. However, the accuracy of this system can be affected by
human, environmental, and equipment variables. It might also be inconvenient that the
thermal imager measures the skin temperature and not the core body temperature [13].
South Korea suffered from the MERS outbreak in 2015. According to Kim K.H., main
contributors to the outbreak were late diagnosis, quarantine failure of ‘super spreaders’,
familial care-giving and visiting non-disclosure by patients, and poor communication by
the South Korean government, inadequate hospital infection management, and ‘doctor
shopping’ [14]. The outbreak was entirely nosocomial, and was largely attributable to infection management and policy failures, rather than biomedical factors. Using multivariate
regression analyses, risk factors such as black race is a risk factor for worse COVID-19
outcome independent of comorbidities, poverty, access to health care, and other mitigating
factors. However, factors affecting case load include lower daily temperatures but do not
affect deaths [15].
From the previous studies, many factors can be linked in the deaths and cases of
COVID-19. In view of the environment, important factors affecting the mortality of the
disease can be temperature variation and humidity [16]. However, there is no evidence
supporting the assertion that case counts of COVID-19 could decline when the weather
becomes warmer [17]. It was also found that there is a significant relationship between air
Int. J. Environ. Res. Public Health 2021, 18, 674
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pollution and COVID-19 infection after controlling for confounding factors [18]. In China,
airflow direction of and air condition was consistent with droplets of the virus which
cause more transmissions in restaurants [19]. In view of sociodemographic factors, number
of cases, sex ratio, disposable income and the average age are significant determinants
of the death rates in the German states [20]. COVID-19 deaths are mainly observed
among elderly males aged over 65, and smoking patients might face a greater risk of
developing into the critical or mortal condition and the comorbidities such as hypertension,
diabetes, cardiovascular disease, obesity, population density and respiratory diseases could
also greatly affect the prognosis of the COVID-19 [21–28]. In contrast, some researchers
found that population density is not an important factor affecting COVID-19 cases or
deaths [29,30].
Rate of infection and the fast transmission of the disease is one of the alarming issues
of the pandemic. In Thailand, the number of tourists and their activities were found to be
significant factors on the number of infected patients from COVID-19 [31]. In Italy, a tighter
lockdown, mobility decreased enough to bring down transmission promptly below the
level needed to sustain the epidemic [32]. However, new measures of social isolation are
being implemented again as Italy experiences a second wave due to the easing preventive
measures [33].
According to the Centers for Disease Control and Prevention (CDC) COVID-19 Response, geographic differences in reported case fatality ratios might also reflect differences
in testing practices; jurisdictions with relatively high proportions of deaths might be those
where testing has been more limited and restricted to the most severely ill [34].
As of today, determination of COVID-19 is now faster with the aid of a more robust
quarantine strategy due to the lessons learned from SARS in 2003 [35]. However, scalable,
rapid and affordable COVID-19 diagnostics could help to limit the spread of SARS-CoV-2,
consequently saving lives [36].
2. Methodology
2.1. Gathering of Data
Information with regards to COVID-19 situations were collected from Worldometer, a
website run by an international team of developers, researchers, and volunteers with the
goal of making world statistics available in a thought-provoking and time relevant format
to a wide audience around the world (https://www.worldometers.info/coronavirus). Information such as demographic characteristics of each country including its gross domestic
product were obtained from the same data source. Weather information were collected
from the website of WeatherBase then average temperature of the countries and its rainfall
were included in the model (Tables 1 and 2).
Table 1. Summary of data source.
Variables
Source
Total Case per country; Total Death per country; Total Test conducted
Worldometer [4]
Population Density; Median Age of the country; Urban Population
Worldometer [37]
Gross Domestic Product
Worldometer [38]
Average Temperature; Average Rainfall
WeatherBase [39]
Table 2. Descriptive statistics of the variables.
Variables
Mean
Median
Maximum
Minimum
Total Case/Million
Tests/Million
Total Tests
Tests/Case
18,670.46
22,0626.70
9,533,267.00
16.17
14,030.00
14,1031.00
2,063,450.00
10.57
63,947.00
1,423,225.00
2.15 × 108
82.16
86.00
2054.00
50,488.00
2.46
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Table 2. Cont.
Variables
Mean
Median
Maximum
Minimum
Deaths/Million
Population Density
Age
Rural Population
GDP/Capita
Temperature
Precipitation
379.91
139.65
33.52
0.33
16,779.46
16.39
874.18
308.00
87.00
33.00
0.31
9881.00
17.00
773.10
1516.00
1380.00
48.00
0.83
80,296.00
40.00
2667.10
3.00
3.00
15.00
0.02
376.00
−0.6
49.50
2.2. Ordinary Least Squares Estimates
In this study, ordinary least squares (OLS) regression were used to identify which
factors might affect the number of cases and deaths from COVID-19. The model is assumed
as follows:
Yit = α0 + βi Xi + ui ,
where i denote the individual indexes of the country. Y is number of cases and number of
deaths from COVID-19, and X is a set of independent variables, e.g., tests per million population (T_PERM), total tests (TT), tests per case (T_PERC), deaths per million population
(D_PERM), population density (PD), median age of the country (AGE), rural population
(RUPOP), GDP per capita (GDP_PERC), raw mortality rate (RMR), average temperature
(TEMP) and average rainfall (RAINF). α0 is the intercept term and β is the slope of the estimated coefficients. The variable u, called the error term or disturbance in the relationship,
represents factors other than x that affect y. (for more details, see Wooldridge 2013 [40].
Raw population density is a poor parameter and the best studies use weighted or lived
population density e.g., [25].
The EVIEWS version 10.0 software was used to perform the estimations. To handle
the heteroskedasticity and autocorrelation, the heteroskedasticity- and autocorrelationconsistent (HAC) robust covariance and standard errors were applied. Since there were
two Y variables, the regression analysis was conducted separately. Empirical model used
for the study are the following:
Total COVID Cases = α0 + β1 T_PERM + β2 TT + β3 T_PERC + β4 D_PERM + β5 PD
+ β6 AGE + β7 RUPOP + β8 GDP_PERC + β9 RMR + β10 TEMP + β11 RAINF + µi
Total COVID Deaths = α0 + β1 T_PERM + β2 TT + β3 T_PERC + β4 PD + β5 AGE
+ β6 RUPOP + β7 GDP_PERC + β8 TEM + β9 RAINF + µi
(1)
(2)
Variables pertinent to the tests in detecting COVID-19 virus such as TT, T_PERM,
which was computed as total tests given divided by one million as represented by the
population of the country and T_PERC were considered to be influencing factors on the
independent variables of the study since it has something to do on the fast detection and
isolation of the infected people. D_PERM was also analyzed as this might indicate that
as many people die from the virus, the possibility of being infected of the other people
is increased. PD, AGE and GDP_PERC were also included since it was found that these
variables significantly affects the transmission of the virus as far as the social distancing
and level of activity of people and their age is a concern [41,42]. RUPOP was computed as
1 minus the percentage of the urban population of the country. The RMR of 1000 population
were also considered and lastly, weather variables such as TEMP and RAINF were captured
since previous studies proves that these might influence our dependent variables [43].
3. Discussion of Results
3.1. Factors Affecting Number of Confirmed Cases from Coronavirus Disease 2019 (COVID-19)
The OLS regression analysis with HAC standard errors and covariance were applied
with Bartlett kernel, and Newey-West fixed bandwidth = 4. The result of the estimation
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is presented in Table 3. It was found that variables such as test per million population,
total tests and test per case are significant at 1% while temperature was significant at 5%.
Test per million has a coefficient −0.56 which means that in every unit in test per million
increase, there is a reduction on the number of cases about 0.516. Total test has coefficient
value of 0.06 which signifies that in every unit of test conducted, there is an increase of
0.6 case on the total case. Test per case has a coefficient of −3374.8 which signifies a negative
relationship on the number of cases. We can infer that the more test being conducted in
relation to every cases detected, there is a decrease on the number of cases by 3374. From all
of the variables in relation to tests, it can be inferred that as we detect more cases, necessary
actions can be done like to isolate people with virus and local transmission will not occur
anymore. Meanwhile, temperature has a coefficient of 30,667.3 which indicates that the
temperature causes more cases if this is being increased.
Table 3. Result of ordinary least squares (OLS) regression analysis on factors affecting number of
cases of coronavirus disease 2019 (COVID-19, based on 10 December 2020 data).
Variable
Coefficient
Std. Error
t-Statistic
Prob.
Constant
Tests/Million
Total Tests
Tests/Case
Deaths/Million
Population Density
Age
Rural Population
GDP/Capita
Raw Mortality Rate
Temperature
Rainfall
−670,837.4
−0.57
0.06
−3374.81
22,659.64
8.39
5801.06
−240,669.9
8.65
−22,056,641
30,667.33
76.80
405074.2
0.21
0.007
1300.57
158,355.8
97.98
6527.42
39,8913
5.35
158,000,000
13,586.81
82.28
−1.656
−2.661
8.471
−2.595
0.143
0.086
0.889
−0.603
1.617
−0.139
2.257
0.933
0.100
0.009 ***
<0.001 ***
0.011 ***
0.886 ns
0.932 ns
0.376 ns
0.547 ns
0.108 ns
0.890 ns
0.026 **
0.352 ns
R-squared
Adjusted R-squared
0.829
0.814
F-statistic
Prob(F-statistic)
56.806
<0.001
Note: ***-Significant at 1%; **-Significant at 5%; ns –not significant.
3.2. Factors Affecting Number of Deaths from COVID-19
Result from the second model can be gleaned in Table 4. At 1% level of significance,
test per million, total tests, test per case are significant variables on the number of deaths
from COVID-19. The coefficient of test per million was −0.016 which specifies that in
every unit of test per million population death from the virus was reduced into 0.016. For
the total tests, only 0.001 death occur if we increase the total test by one unit. When the
tests/confirmed cases ratio increased with one unit, it leads to 68.44 deaths reduction.
Meanwhile, Age, Rural population, and temperature are found to be significant at 10%.
The coefficient value of the median age of the country is 365 which means that as people
grow older in particular area number of death is increased by 365. Rural population can be
interpreted such that in every percentage increase on this variable causes a reduction of
236 deaths. For the temperature, an additional 648 death case will be added if there is an
increase in every unit of degree Celsius.
Table 4. Result of OLS regression analysis on factors affecting number of deaths of COVID-19
(10 December 2020 data).
Variable
Coefficient
Std. Error
t-Statistic
Prob.
Constant
Test/Million
Total Test
Test/Case
−4989.98
−0.016
0.001
−68.44
9446.07
0.007
0.00
22.62
−0.528
−2.446
7.105
−3.025
0.598
0.016 ***
<0.001 ***
0.003 ***
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Table 4. Cont.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
Population Density
Age
Rural Population
GDP/Capita
Temperature
Rainfall
−3.10
365.04
−23,696.37
0.10
648.08
0.17
3.38
208.84
13,939.86
0.11
356.77
1.85
−0.918
1.748
−1.700
0.862
1.817
0.092
0.361 ns
0.083 *
0.092 *
0.391 ns
0.072 *
0.927 ns
R-squared
Adjusted R-squared
0.690
0.669
F-statistic
Prob(F-statistic)
32.370
<0.001
Note: ***-Significant at 1%; *-Significant at 10%; ns –not significant.
3.3. Cases and Deaths from COVID-19 in Countries with High and Low Population
Countries were group together according to the number of population to compare the
variables which factors significantly affect their COVID-19 situations. Those countries with
a population of 10 million and above were defined as the high population group while
countries with population below 10 million were defined as the low population group.
There were at least 77 countries who belongs to the high population group and a total of
64 countries for low population.
Comparing the two groups, it can be observed that in high population countries
variables that are significant at 1% tests per million, total tests and tests per case with a
coefficient of −2.434, 0.065, and −4275.8, respectively (Table 5). While low population
country has a significant variables of Test per million (−0.22) and total test (0.03). Tests per
case appeared to be non-significant variable. Based from the figures, there is less effect of
tests per million on the number of cases in a low population countries. While a little bit
higher effects of total tests in a high population area.
Meanwhile, population density and rural population were found to be significant
factors at 5% on the number of cases for the low population areas. Population density has a
coefficient of 75.6 which means that an increase in population density causes an additional
75.6 cases. Rural population on the other hand has a negative relationship on the number
of cases, a total of 77,901.50 will be reduced if one percentage in rural population is added.
Looking at the goodness of the fit of the model, R2 for the high population countries has a
value of 0.87 which means that 87% of the total variation is explained by the factor included
in the regression analysis. The value of R2 of low population groups was 0.66.
Table 5. Result of OLS regression analysis on factors affecting number of cases of COVID-19.
High Population Areas
Variable
Coeff.
S. E.
t-Stat.
Low Population Areas
Prob.
ns
Coeff.
S. E.
t-Stat.
Prob.
48,850.86
0.07
0.008
55.52
23,887.39
28.84
1231
37,918.24
0.73
2,390,6984
1365.54
8.98
1.988
−3.048
3.781
−1.581
0.175
2.622
0.088
−2.054
1.665
−0.168
−1.123
−1.331
0.052
0.004 ***
<0.001 ***
0.120 ns
0.862 ns
0.011 **
0.930 ns
0.045 **
0.102 ns
0.867 ns
0.267 ns
0.189 ns
Constant
Test /Million
Total Test
Test/Case
Death/Million
Pop. Density
Age
Rural Population
GDP/Capita
RMR
Temperature
Rainfall
−1,527,543
−2.434
0.065
−4275.8
65,324.64
−257.49
16,555.06
202,975.9
17.33
−64,046,492
52,773.07
45.37
786,425.2
1.38
0.007
1515.14
29,6146.5
333.54
11,249.27
577,967.10
10.84
296,000,000
25,077.25
120.20
−1.942
−1.760
9.779
−2.822
0.221
−0.772
1.472
0.351
1.599
−0.216
2.104
0.377
0.056
0.083 **
0.00 ***
0.006 ***
0.826 ns
0.443 ns
0.146 ns
0.727 ns
0.115 ns
0.830 ns
0.039 **
0.707 ns
97,110.06
−0.22
0.03
−87.79
4171.76
75.60
108.29
−77,901.50
1.22
−4,024,853
−1532.85
−11.95
R2
Adj. R2
0.874
0.853
F-stat.
Prob.
41.157
<0.001
R2
Adj. R2
0.668
0.598
Note: ***-Significant at 1%; **-Significant at 5%; ns –not significant.
F-statistic
Prob(F-statistic)
9.540
<0.001
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For the number of deaths, the results of the regression of the second model comparing
the two groups of countries is presented at Table 6. In this estimation, at 1% level of
significance variables that significantly affect the number of death from COVID-19 for
the high population countries are total tests and tests per case with a coefficient of 0.01
and −82.55, respectively. On the low population countries’ tests per million, total tests,
tests per case and age were found to be significant at 1%. As far as total test effects is
concerned, two groups have the same negative sign on the coefficient and but are dissimilar
in terms of coefficient. The effect of tests per case is lower in low populations with a value
of −3.4 comparable to higher populations with a coefficient of −82.55. As the age of people
increase it signifies that there are additional deaths of 72.94. At 10% level of significance,
population density has an effect 0.95 increase in deaths as its unit increases and rural
population have been found to be negatively significant with 12.94 death cases reduction if
1 more percentage people resides on the country side.
The R2 value was observed to be lower at low population areas than higher population
groups with a value of 0.46 and 0.71, respectively.
Table 6. Result of OLS regression analysis on factors affecting number of deaths of COVID-19.
High Population Areas
Low Population Areas
Variable
Coeff.
S.E.
t-stat.
Prob.
Coeff.
S. E.
t-stat.
Prob.
Constant
Tests/M
Total Tests
Tests/Case
Pop. Density
Age
Rural Pop.
GDP/Capita
Temperature
Rainfall
−5375.87
−0.02
0.001
−82.55
−13.69
373.21
−35,365.37
0.20
1129.91
0.21
15,519.28
0.02
0.00
24.56
10.66
382.80
22,396.51
0.28
683.76
4.20
−0.346
−0.748
8.519
−3.361
−1.284
0.975
−1.579
0.718
1.653
0.052
0.730
0.457 ns
<0.001 ***
<0.001 ***
0.204 ns
0.333 ns
0.119 ns
0.475 ns
0.103 ns
0.959 ns
180.55
−0.004
0.0004
−3.45
0.95
72.95
−1294.06
0.008
−42.89
0.17
898.98
0.001
0.00
0.91
0.51
24.35
733.14
0.01
26.06
0.16
0.201
−3.240
4.047
−3.809
1.877
2.996
−1.765
0.548
−1.646
1.081
0.842
0.002 ***
<0.001 ***
<0.001 ***
0.066 *
0.004 ***
0.083 *
0.586 ns
0.106 ns
0.285 ns
R2
Adj. R2
0.721
0.684
19.257
<0.001
R2
Adj. R2
0.468
0.379
F-stat.
Prob (F-stat)
5.280
<0.001
F-stat.
Prob.(F-stat)
Note: ***-Significant at 1%; *-Significant at 10%; ns –not significant.
4. Conclusions
In view of the series of estimations conducted, the researchers draw the following
conclusions: first, COVID-19 tests play an important role in our battle against this pandemic.
The relationship of tests on cases and deaths were consistently significant even in countries
with different levels of population. Carrying out more tests allows the authorities to detect
more infections and do all the important steps in preventing infected people to have local
transmissions. Although tests were also positively associated with deaths, its coefficient
was good enough, which indicates that less deaths will occur as long as we do the isolation
due to the early detection of the infected people. Second, age is a significant factor on the
number of deaths. The findings shows that it is very important to protect older people from
the exposure to more people as they are at risk of being infected by the virus. Third, rural
population has negative effects on death. This is an advantage for countries which have
a larger percentage of population in the countryside. This gives more people space and
lessens the risk of catching COVID-19. Fourth, the temperature was positively significant
for both number of cases and deaths. The mean temperature in our observation is 16.39
degree Celsius. Although +14.51 ◦ C temperature is the favorable range for COVID-19
growth [44], temperature that is favorable for people to move out increases their chances to
be exposed to the virus. A strict implementation of the safety protocols must be undertaken.
The effect of population density was observed both on high and low population countries
as significant factors on deaths. This is evidence that anywhere in the world, the higher the
population density the riskier the threat of the virus.
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Based on the results, it is suggested that the government may consider allocating a
higher budget to testing equipment and facilities to fight this pandemic victoriously. A
strict implementation of protocols with regards to social distancing and wearing of face
masks is also advised in order to lessen the transmission of the virus. Nevertheless, each
country may have its own best strategy depending on the availability of resources and
funds that they have. The number of confirmed cases is globally underestimated, and
comparisons across countries are difficult to determine due to differences in data collection
procedures or health policies, among others. Thus, this statistical analysis of COVID-19
data needs to be interpreted with caution.
Author Contributions: Conceptualization J.M.V., W.-C.T.; methodology, W.-C.T., J.M.V., C.-L.C.;
formal analysis, J.M.V., W.-C.T.; data curation, J.M.V.; writing—original draft preparation, J.M.V.;
writing—review and editing J.M.V., C.-L.C., W.-C.T.; supervision, C.-L.C., W.-C.T. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments: The authors would like to thank the anonymous reviewers who extended their
time and efforts by giving their valuable comments for the improvement of this paper.
Conflicts of Interest: The authors declare no conflict of interest
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